Effective project portfolio management for SMEs: A conceptual framework using business intelligence tools

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F25%3A00378538" target="_blank" >RIV/68407700:21220/25:00378538 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.procs.2025.01.136" target="_blank" >https://doi.org/10.1016/j.procs.2025.01.136</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2025.01.136" target="_blank" >10.1016/j.procs.2025.01.136</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Effective project portfolio management for SMEs: A conceptual framework using business intelligence tools

Popis výsledku v původním jazyce

This article investigates the integration of project management and business intelligence (BI) tools to improve operations in project based SMEs. While these implementations can improve efficiency and decision-making, they are generally prohibitively expensive for resource-constrained SMEs. Existing approaches are primarily aimed at larger enterprises, leaving a need for scalable, cost effective solutions for SMEs. As a result, this study assesses current implementations and suggests an alternative approach. This platform, which is built around a neural network trained on a bespoke data model and a chatbot for data collection, makes project management easier even for those with limited BI knowledge. It is being tested in selected mechanical engineering SMEs with the goal of improving performance, lowering risks, and increasing project success, overcoming the limits of current methodologies and providing SMEs with a competitive advantage.

Název v anglickém jazyce

Effective project portfolio management for SMEs: A conceptual framework using business intelligence tools

Popis výsledku anglicky

This article investigates the integration of project management and business intelligence (BI) tools to improve operations in project based SMEs. While these implementations can improve efficiency and decision-making, they are generally prohibitively expensive for resource-constrained SMEs. Existing approaches are primarily aimed at larger enterprises, leaving a need for scalable, cost effective solutions for SMEs. As a result, this study assesses current implementations and suggests an alternative approach. This platform, which is built around a neural network trained on a bespoke data model and a chatbot for data collection, makes project management easier even for those with limited BI knowledge. It is being tested in selected mechanical engineering SMEs with the goal of improving performance, lowering risks, and increasing project success, overcoming the limits of current methodologies and providing SMEs with a competitive advantage.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

50204 - Business and management

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Procedia Computer Science

ISBN

—

ISSN

1877-0509

e-ISSN

1877-0509

Počet stran výsledku

12

Strana od-do

745-756

Název nakladatele

Elsevier B.V.

Místo vydání

Amsterdam

Místo konání akce

Praha

Datum konání akce

20. 11. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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